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Discussion papers
https://doi.org/10.5194/tc-2019-14
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2019-14
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Feb 2019

Research article | 07 Feb 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).

Automatically delineating the calving front of Jakobshavn Isbræ from multi-temporal TerraSAR-X images: a deep learning approach

Enze Zhang, Lin Liu, and Lingcao Huang Enze Zhang et al.
  • Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China

Abstract. The calving fronts of many tidewater glaciers in Greenland have been undergoing strong seasonal and inter-annual fluctuations. Conventionally, calving front positions have been manually delineated from remote sensing images. But manual practices can be labor-intensive and time-consuming, particularly when processing a large number of images taken over decades and covering large areas with many glaciers, such as Greenland. Applying U-Net, a deep learning architecture, to multi-temporal Synthetic Aperture Radar images taken by the TerraSAR-X satellite, we here automatically delineate the calving front positions of Jakobshavn Isbræ from 2009 to 2015. Our results are consistent with the manually delineated products generated by the Greenland Ice Sheet Climate Change Initiative project. We show that the calving fronts of Jakobshavn's two main branches retreated at mean rates of −117 ± 1 m yr−1 and −157 ± 1 m yr−1, respectively, during the years 2009 to 2015. The inter-annual calving front variations can be roughly divided into three phases for both branches. The retreat rates of the two branches tripled and doubled, respectively, from phase 1 (April 2009–January 2011) to phase 2 (January 2011–January 2013), then stabilized nearly zero in phase 3 (January 2013–December 2015). We suggest that the retreat of the calving front into an overdeepened basin whose bed is retrograde may have accelerated the retreat after 2011, while the inland-uphill bed slope behind the bottom of the overdeepened basin has prevented the glacier from retreating further after 2012. Demonstrating through this successful case study on Jakobshavn Isbræ and due to the transferable nature of deep learning, our methodology can be applied to many other tidewater glaciers both in Greenland and elsewhere in the world, using multi-temporal and multi-sensor remote sensing imagery.

Enze Zhang et al.
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Enze Zhang et al.
Data sets

The calving fronts delineated by the network in Jakobshavn Isbræ E. Zhang, L. Liu, and L. Huang https://doi.org/10.1594/PANGAEA.897064

Video supplement

Calving front variations in Jakobshavn Isbræ E. Zhang, L. Liu, and L. Huang https://doi.org/10.1594/PANGAEA.897062

The comparison between calving front variations and bed elevation of Branch A in Jakobshavn Isbræ E. Zhang, L. Liu, and L. Huang https://doi.org/10.1594/PANGAEA.897080

The comparison between calving front variations and bed elevation of Branch B in Jakobshavn Isbræ E. Zhang, L. Liu, and L. Huang https://doi.org/10.1594/PANGAEA.897063

Enze Zhang et al.
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Short summary
Conventionally, calving front positions have been manually delineated from remote sensing images. We design a novel method to automatically delineate the calving front positions of Jakobshavn Isbræ based on deep learning, the first of this kind for Greenland outlet glaciers. We generate high temporal resolution (about two measurements every month) calving fronts. Demonstrating through this successful case study on Jakobshavn Isbræ, our methodology can be applied to many other tidewater glaciers.
Conventionally, calving front positions have been manually delineated from remote sensing...
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